Multi Modal LLM
Multi-modal large language models (MLLMs) aim to integrate diverse data modalities, such as text, images, audio, and video, into a unified framework for enhanced understanding and generation. Current research focuses on improving MLLM performance through techniques like instruction tuning, developing novel architectures (e.g., incorporating diffusion models or retrieval-augmented generation), and addressing challenges such as bias, hallucination, and efficient fine-tuning. The development of robust and reliable MLLMs holds significant potential for advancing various fields, including healthcare (e.g., medical image analysis), autonomous driving, and financial technology (e.g., fraud detection), by enabling more sophisticated and context-aware applications.